Collaborative Research: OAC Core: Simulation-driven runtime resource management for distributed workflow applications

在健康、气候建模、粒子物理、地震学等领域，许多科学突破只能通过对大量数据进行复杂处理来实现。软件系统使用网络基础设施提供的计算、存储和网络硬件来自动处理这个过程。除了自动化之外，这些系统的一个关键目标是通过成本和能源使用来有效利用资源，同时尽可能快地或根据需要进行处理。为此，这些系统必须决定应该使用哪些资源来做什么和什么时候做什么。许多这样的系统在今天的生产中使用，并做出这样的决策。然而，做出好的决策，更不用说最好的决策，仍然是一个开放的研究挑战。理论研究提出了难以付诸实践的解决方案，而众所周知，实际的解决方案不会做出正确的决策，或者至少不会始终如此。但是，理论和实践都遵循相同的基本原理：通过对需要计算的内容和可用硬件资源的已知信息进行推理来做出决策。这种理念已经显示出它的局限性，所以这个项目采用了一种完全不同的方法。关键思想是重复执行快速、计算成本低廉的应用程序执行模拟，以便评估大量潜在的资源管理决策，并自动选择最理想的决策。这种方法的好处将在几个软件系统中得到证明，这些软件系统用于支持对社会发展和可持续发展至关重要的科学应用。软件系统用于在先进的网络基础设施上运行科学应用程序。这些系统自动执行应用程序，并沿着几个轴做出资源管理决策，包括选择和供应（虚拟化）硬件、选择应用程序配置选项以及在时间和空间上调度应用程序活动。他们的目标是优化应用程序性能和一组资源使用效率指标（包括货币和能源成本）。因此，资源管理决策空间是巨大的，做出好的决策是一项艰巨的挑战，已经成为理论家和实践者无数努力的主题。然而，这一挑战远未得到解决：理论家提出的解决方案很少被实践者使用，相反，实践者实施的解决方案可能非常不理想，因为他们没有得到理论的支持。该项目通过消除开发有效资源管理策略的需要来解决这种脱节。其关键思想是利用在线模拟在运行时快速搜索资源管理决策空间。在整个执行过程中，对应用程序的执行进行大量快速模拟，以便评估许多潜在的资源管理选项并自动选择理想的选项。因此，这种方法将整个问题从复杂资源管理算法的设计转移到许多资源管理决策的枚举。网络基础设施系统资源管理实践的转变不仅使资源管理问题变得容易处理，而且解开了以前遥不可及的资源管理决策。这种转变的好处将在生产系统和应用程序的关键类别中得到证明，特别是工作流管理系统和它们支持的科学应用程序。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many scientific breakthroughs in domains such as health, climate modeling, particle physics, seismology, etc., can only be achieved by performing complex processing of vast amounts of data. This processing is automated by software systems that use the compute, storage, and network hardware provided by the cyberinfrastructure. In addition to automation, a key objective of these systems is the efficient use of the resources as measured by cost and energy usage, while making the processing as fast as possible or as needed. To this end, these systems must make decisions regarding which resources should be used to do what and when. Many such systems are used in production today and make such decisions. Yet making good, let alone best, decisions is still an open research challenge. Theoretical research has proposed solutions that are difficult to put into practice, and practical solutions are known to not make good decisions, or at least not consistently so. However, both theory and practice follow the same basic philosophy: make decisions by reasoning about known information on what needs to be computed and on what hardware resources are available. This philosophy has shown its limits, so this project adopts a radically different approach. The key idea is to repeatedly execute fast, computationally inexpensive simulations of the application execution in order to evaluate large sets of potential resource management decisions and automatically select the most desirable ones. The benefits of this approach will be demonstrated for several software systems used to support scientific applications that are critical for the development and sustainability of society.Software systems are used to run scientific applications on advanced cyberinfrastructure. These systems automate application execution, and make resource management decision along several axes including selecting and provisioning (virtualized) hardware, picking application configuration options, and scheduling application activities in time and space. Their objective is to optimize both application performance and also a set of resource usage efficiency metrics that include monetary and energy costs. Consequently, the resource management decision space is enormous, and making good decisions is a steep challenge that has been the subject of countless efforts, both from theoreticians and practitioners. However, the challenge is far from being solved: theoreticians produce solutions that are rarely used by practitioners, and conversely practitioners implement solutions that may be highly sub-optimal because they not informed by theory. This project resolves this disconnect by obviating the need for developing effective resource management strategies. The key idea is to use online simulations to search the resource management decision space rapidly at runtime. Large numbers of fast simulations of the application's execution are executed throughout that very execution, so as to evaluate many potential resource management options and automatically select desirable ones. This approach thus shifts the overall problem from the design of complex resource management algorithms to the enumeration of many resource management decisions. The transformation of resource management practice in cyberinfrastructure systems not only renders the resource management problem tractable but also unlocks previously out-of-reach resource management decisions. The benefits of this transformation will be demonstrated for a critical class of production systems and applications, specifically Workflow Management Systems and the scientific applications they support.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.